ITR Collaborative Research: Biometrics -- Performance, Security and Social Impact

ITR 协作研究：生物识别——性能、安全性和社会影响

• 批准号: 0325333
• 负责人: Stephanie Schuckers
• 金额: --
• 依托单位: Clarkson University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-15 至 2011-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325333&HistoricalAwards=false
• 关键词: ITR; Collaborative; Research; Biometrics; Performance

PI: Lawrence A. Hornak, West Virginia UniversityBiometric systems automatically measure a physiological or behavioral "signature" of an individual, from which a decision can be rendered to either authenticate or determine the identity of that individual. In this way, biometric systems provide the means to bind the physical presence of an individual user with her cyber action. Despite the increasing use of these systems, neither an analytic framework for modeling and predicting their performance, nor a set of standard data available to researchers for testing purposes is available. This research addresses these deficiencies by determining an analytic framework in which the performance of biometric systems can be modeled and predicted, removing key barriers to biometric system performance through research in multibiometrics and effective vulnerability countermeasures, and understanding the relationships among biometric applications, privacy, security, and user acceptance that are essential for both informed public policy and system design. Addressing this interrelated set of key technical barriers and societal issues will advance the understanding of biometric system acceptance, performance, and design, and will enable trustworthy authentication and identification functions essential to pervasive computing and supporting homeland security needs. The data sets generated by the coordinated tasks of this research are also being made available as a resource to the biometrics research, developer, and user communities.

PI: Lawrence a . Hornak，西弗吉尼亚大学生物识别系统自动测量一个人的生理或行为“签名”，由此可以做出一个决定，要么验证，要么确定这个人的身份。通过这种方式，生物识别系统提供了将个人用户的实际存在与她的网络行为绑定在一起的手段。尽管这些系统的使用越来越多，但既没有一个建模和预测其性能的分析框架，也没有一组可供研究人员用于测试目的的标准数据。本研究通过确定一个分析框架来解决这些缺陷，在这个框架中，生物识别系统的性能可以建模和预测，通过研究多生物识别技术和有效的脆弱性对策来消除生物识别系统性能的主要障碍，并理解生物识别应用、隐私、安全和用户接受度之间的关系，这对知情的公共政策和系统设计都是至关重要的。解决这些相互关联的关键技术障碍和社会问题将促进对生物识别系统的接受、性能和设计的理解，并将使可信的身份验证和识别功能成为普及计算和支持国土安全需求所必需的。由本研究的协调任务产生的数据集也作为资源提供给生物识别研究、开发人员和用户社区。